发表机构
Southeast University; The Hong Kong University of Science and Technology (Guangzhou); Shanghai University of Electric Power（东南大学; 香港科技大学（广州）; 上海电力大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于采样的扰动观测器（SDOB），通过状态展开或代价查询接口扩展DOB原理至仿真器和学习世界模型，分离状态效应与代价扰动，实验验证其能有效补偿仿真到现实不匹配并提升控制性能。
AI 中文摘要
机器人控制器日益依赖解析模型、仿真器、代价查询接口和学习世界模型。然而，物理部署可能偏离名义假设，即使模型本身准确，也可能出现额外的扰动。在控制系统中，扰动观测器（DOB）被广泛用于从名义模型和测量反馈中估计此类未测量的效应。经典DOB公式通常围绕显式被控对象模型构建。本文开发了基于采样的扰动观测器（SDOB），通过状态展开或代价查询接口，将DOB原理扩展到更广泛的模型，包括仿真器和学习世界模型。SDOB分离两个可观测通道：状态效应扰动（反馈状态与其预测不同）和代价扰动（同一查询状态因感知环境变化而获得不同代价）。跨传统模型和学习模型的多种仿真及真实机器人实验证明了SDOB在补偿仿真到现实不匹配和提升控制性能方面的有效性。
英文摘要
Robotic controllers increasingly rely on analytical models, simulators, cost-query interfaces, and learned world models. However, physical deployment can deviate from nominal assumptions, and additional disturbances may arise even when the model itself is accurate. In control systems, disturbance observers (DOB) are widely used to estimate such unmeasured effects from nominal models and measured feedback. Classical DOB formulations are generally built around explicit plant models. This paper develops the sampling-based disturbance observer (SDOB), extending the DOB principle to a broader range of models, including simulators and learned world models, through state-rollout or cost-query interfaces. SDOB separates two observable channels: state-effect disturbances, for which the feedback state differs from its prediction, and cost disturbances, for which the same query state receives different costs as the perceived environment changes. Diverse simulation and real-robot experiments across traditional and learned models demonstrate the effectiveness of SDOB in compensating for sim-to-real mismatch and improving control performance.
Comments15 pages, 14 figures, 7 tables. Project website: https://sampling-based-dob.github.io/
